{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Initialize the environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import theano\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "src_dir='../src' # source directory\n",
    "run_dir='../MDBN_run' # directory with the results of previous runs\n",
    "data_dir='../data' # directory with the data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, src_dir)\n",
    "import MDBN\n",
    "import AMLsm2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the experiment results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding a layer with 559 input and 40 outputs\n",
      "Adding a layer with 19937 input and 400 outputs\n",
      "Adding a layer with 400 input and 40 outputs\n",
      "Adding a layer with 1686 input and 200 outputs\n",
      "Adding a layer with 200 input and 20 outputs\n",
      "Adding a layer with 100 input and 24 outputs\n",
      "Adding a layer with 24 input and 3 outputs\n"
     ]
    }
   ],
   "source": [
    "date_time='2016-12-30_1830' # specify the date and time of the run in the format YYYY-MM-DD_HHMM\n",
    "\n",
    "runfile='Run_'+date_time+'/Exp_'+date_time+'_run_0.npz' # location of the experiment results\n",
    "me_DBN, ge_DBN, sm_DBN, dm_DBN, top_DBN = AMLsm2.load_network(runfile,run_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the results graphically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datafiles = AMLsm2.prepare_AML_TCGA_datafiles(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ME_output, _ = me_DBN.MLP_output_from_datafile(datafiles['ME'], datadir=data_dir)\n",
    "GE_output, _ = ge_DBN.MLP_output_from_datafile(datafiles['GE'], datadir=data_dir)\n",
    "SM_output, _ = sm_DBN.MLP_output_from_datafile(datafiles['SM'], datadir=data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 99.5, 169.5, -0.5)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tNzNaQDTagYmZ94gWhbnkkkuqtojmxUY22PWMpg/2YJ77tm3bqj6HH3546Hoe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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12589b0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "joint_layer = np.concatenate([ME_output, GE_output, SM_output],axis=1)\n",
    "plt.imshow(joint_layer, cmap='gray',interpolation='none')\n",
    "plt.axis('tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x12591ddd0>,\n",
       "  <matplotlib.axis.XTick at 0x12c536dd0>,\n",
       "  <matplotlib.axis.XTick at 0x12c536410>],\n",
       " <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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t674HuIAsG34MuAo4rema1wC75j//DvC2/Oe78n+KbJz9LcD3On2wA7uZmdXK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\n4sBuZmZWI/8f8A5u7mxA15MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1258f5a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_output = top_DBN.get_output(theano.shared(joint_layer,borrow=True))\n",
    "plt.imshow((top_output>0.8)*np.ones_like(top_output)-(top_output<0.2)*np.ones_like(top_output),interpolation='none',extent=[0,3,385,0])\n",
    "plt.colorbar()\n",
    "plt.axis('tight')\n",
    "plt.xticks(np.arange(0.5,3.5,1),('0','1','2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x11a0b3cd0>,\n",
       "  <matplotlib.axis.XTick at 0x11a0e8b90>,\n",
       "  <matplotlib.axis.XTick at 0x11a228a10>],\n",
       " <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SkXQqcCmwGDicJFGvkTSjyXWvA64Dbunkvl3Vot4F7Cw6CDObcHsXHYDlZq8c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\nMzMrMSdqMzOzEvv/ASL/hC7Om+BOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1162805d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(top_output, interpolation='none',extent=[0,3,385,0])\n",
    "plt.axis('tight')\n",
    "plt.colorbar()\n",
    "plt.xticks(np.arange(0.5,3.5,1),('0','1','2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([  35.,    7.,    2.,    1.,    0.,    0.,    0.,    3.,    3.,  119.]),\n",
       "  array([ 154.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,   16.]),\n",
       "  array([ 108.,    2.,    1.,    1.,    0.,    1.,    0.,    1.,    2.,   54.])],\n",
       " array([  3.06261703e-16,   1.00000000e-01,   2.00000000e-01,\n",
       "          3.00000000e-01,   4.00000000e-01,   5.00000000e-01,\n",
       "          6.00000000e-01,   7.00000000e-01,   8.00000000e-01,\n",
       "          9.00000000e-01,   1.00000000e+00]),\n",
       " <a list of 3 Lists of Patches objects>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8F4YkSapmgJAkSdUMEJIkqZoBQpIkVTNASJKkagYISZJUzQAhSZKqGSAkSVI1A4QkSapm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\nmgFCkiRVM0BIkqRqBghJklTt/wM2/DUv7H59CQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a1444d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(top_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "code = (top_output[:,0:3] > 0.5) * np.ones_like(top_output[:,0:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from utils import find_unique_classes\n",
    "U = find_unique_classes(code)\n",
    "cl = U[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.,  0.,  0.,  0.,  0.,  3.,  3.,  2.,  1.,  2.,  1.,  2.,  0.,\n",
       "        2.,  2.,  2.,  2.,  4.,  3.,  1.,  1.,  0.,  3.,  3.,  1.,  0.,\n",
       "        3.,  1.,  1.,  2.,  2.,  4.,  4.,  0.,  3.,  2.,  2.,  3.,  2.,\n",
       "        3.,  2.,  3.,  0.,  1.,  2.,  2.,  2.,  2.,  3.,  3.,  4.,  0.,\n",
       "        2.,  3.,  0.,  2.,  2.,  3.,  2.,  4.,  1.,  2.,  2.,  2.,  2.,\n",
       "        2.,  3.,  2.,  1.,  2.,  2.,  2.,  1.,  3.,  2.,  3.,  2.,  2.,\n",
       "        4.,  2.,  2.,  3.,  2.,  2.,  2.,  3.,  2.,  3.,  1.,  0.,  2.,\n",
       "        0.,  3.,  1.,  0.,  3.,  2.,  3.,  3.,  3.,  3.,  3.,  2.,  2.,\n",
       "        2.,  2.,  3.,  0.,  3.,  2.,  2.,  3.,  2.,  2.,  2.,  0.,  3.,\n",
       "        2.,  2.,  3.,  2.,  2.,  3.,  3.,  0.,  3.,  0.,  0.,  2.,  0.,\n",
       "        0.,  1.,  1.,  0.,  0.,  2.,  1.,  2.,  0.,  3.,  0.,  3.,  4.,\n",
       "        1.,  4.,  0.,  3.,  1.,  2.,  1.,  2.,  2.,  4.,  2.,  2.,  4.,\n",
       "        2.,  2.,  4.,  4.,  2.,  4.,  2.,  3.,  2.,  4.,  2.,  2.,  2.,  4.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 26.,  19.,  70.,  39.,  16.]),\n",
       " array([-0.5,  0.5,  1.5,  2.5,  3.5,  4.5]),\n",
       " <a list of 5 Patch objects>)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AkgTYBdxVVV/vN2+mFwiW13Rf7m+TJEkToNMlgDU+BLwc+KcjqkWSJB0nAwWAJB8EXgdc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\n0E0NMeBohAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c57e4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_cl = np.max(cl)\n",
    "plt.hist(cl,bins=np.arange(-0.5,max_cl + 1.5,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check Survival curves for the different classes\n",
    "==============================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "id=[]\n",
    "with open('../data/'+datafiles['ME']) as f:\n",
    "    my_csv = csv.reader(f,delimiter='\\t')\n",
    "    id = my_csv.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stat={}\n",
    "with open('../data/AML/AML_clinical_data2.csv') as f:\n",
    "    reader = csv.reader(f, delimiter=',')\n",
    "    for row in reader:\n",
    "        patient_id=row[0]\n",
    "        stat[patient_id]=(row[4],row[7],row[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following case IDs were  not found in clinical data\n",
      "No data for TCGA-AB-2887\n",
      "No data for TCGA-AB-2891\n",
      "No data for TCGA-AB-2918\n",
      "No data for TCGA-AB-2921\n",
      "No data for TCGA-AB-2930\n",
      "No data for TCGA-AB-2940\n",
      "No data for TCGA-AB-2943\n",
      "No data for TCGA-AB-2946\n",
      "No data for TCGA-AB-2975\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "time_list = []\n",
    "event_list = []\n",
    "group_list = []\n",
    "print('The following case IDs were  not found in clinical data')\n",
    "for index, key in enumerate(id[1:]):\n",
    "    m = re.match('TCGA-\\w+-\\d+', key)\n",
    "    patient_id = m.group(0)\n",
    "    if patient_id in stat:\n",
    "        patient_stat = stat[patient_id]\n",
    "        add_group = True\n",
    "        try:\n",
    "            time_list.append(float(patient_stat[2]))\n",
    "            event_list.append(1)\n",
    "        except ValueError:\n",
    "            try:\n",
    "                time_list.append(float(patient_stat[1]))\n",
    "                event_list.append(0)\n",
    "            except ValueError:\n",
    "                print('No data for %s' % patient_id)\n",
    "                add_group = False\n",
    "        if add_group:\n",
    "            group_list.append(cl[index])\n",
    "    else:\n",
    "        print(patient_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112a86590>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sx8XAf4TbSW/nvcAKd/9e5FhaP9OD2priz7U6vVPC/wDbCN4Urwbm1vqN7iDb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\ngriISIIpiIuIJJiCuIhIgimIi4gk2P8HcgdpOWgfII4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112ab5b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifelines import KaplanMeierFitter\n",
    "kmf = KaplanMeierFitter()\n",
    "kmf.fit(time_list,event_observed=event_list)\n",
    "kmf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YE3sYXSL7hsUvTBxvijtAa1ERr9cEi2OW+QOcc/Bw3LZlePlGIPYVt4Hwe2V4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1199ccd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T=np.array(time_list)\n",
    "E=np.array(event_list)\n",
    "ix = (np.array(group_list) == 0)\n",
    "kmf.fit(T[ix], E[ix], label='group 0')\n",
    "ax=kmf.plot()\n",
    "for i in range(1,5):\n",
    "    ix=(np.array(group_list)==i)\n",
    "    kmf.fit(T[ix], E[ix], label='group %d' % i)\n",
    "    kmf.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}